Warp Speed: Vaccine Rollout

## Our World In Data Vaccine Plots
rm(list=ls())
source("../DATA/movavg.R")
db <- db <- dbConnect(RSQLite::SQLite(),dbname= "../COVID-19-DB/OURWORLD.sqlite3")
SWE <- dbGetQuery(db,"select * from OWID")
SWE$date <- as.Date(SWE$date)
SWE <- SWE[order(SWE$date),]
df <- SWE %>%select(date,location,iso_code,new_vaccinations,
                    new_vaccinations_smoothed,total_vaccinations,population)
                    

dbDisconnect(db)

US Vaccnations By Date

US <- df %>% filter(location =="United States") %>% na.omit()
summary(US$new_vaccinations)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   57909  651669  878059  886713 1125552 1561585

p5 <- ggplot(US) + geom_line(aes(x=date,y=new_vaccinations)) +
  scale_y_continuous(labels=comma) +
  labs(title="US New Vaccinations By Date") +
  geom_smooth(aes(x=date,y=new_vaccinations))

p6 <- ggplot(US) + geom_line(aes(x=date,y=total_vaccinations)) +
  scale_y_continuous(labels=comma) +
  labs(title="US Total Vaccinations By Date") 

US$Rate <- US$total_vaccinations/US$population
p7 <- ggplot(US) + geom_line(aes(x=date,y=Rate)) +
  scale_y_continuous(labels=percent) +
  labs(title="US vaccination Rate By Day")
ggplotly(p5)

ggplotly(p6)

ggplotly(p7)